/*
 * Copyright (c) 2013, Alex Theodoridis <email>
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *     notice, this list of conditions and the following disclaimer in the
 *     documentation and/or other materials provided with the distribution.
 *     * Neither the name of the <organization> nor the
 *     names of its contributors may be used to endorse or promote products
 *     derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY Alex Theodoridis <alekstheod@gmail.com> ''AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL Alex Theodoridis <email> BE LIABLE FOR ANY
 * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 */

#ifndef NODE_H
#define NODE_H

#include <vector>
#include <array>
#include <Utilities/System/Time.h>
#include <NeuralNetwork/SOM/IPosition.h>
#include <NeuralNetwork/NNException.h>
#include <Utilities/Math/Math.h>
#include <iostream>


namespace nn
{

namespace kohonen
{

template< typename PositionType, unsigned int inputsNumber = 10>
class KNode
{
public:
    typedef IPosition< PositionType > Position;
    typedef typename Position::Var Var;
    typedef std::array< Var, inputsNumber > InputType;
      
public:
    /// @brief rebind the current node to a different number of inputs.
    template<unsigned int numOfInputs>
    struct rebindInputsNumber{
      typedef KNode< PositionType, numOfInputs > Type;
    };
  
private:
    Position m_position;
    InputType m_weights;

public:
    /**
     * @brief constructor will initialize the object.
     * @param inputsNumber the number of the inputs for the current object.
     * @param position a position inside the map.
     */
    KNode (const Position& position ) :m_weights({Var(0.f), Var(0.f), Var(0.f)}), m_position ( position ) {
        Var weight ( 0.0f );
        for ( unsigned int i = 0; i < inputsNumber; i++ ) {
            weight = ( Var( utils::createRandom<Var>(1) ) );
            m_weights[i] = weight;
        }
    }

    /**
     * @brief Will calculate the distance of the input from the input weight.
     * @param inputs the list of the input values.
     * @return the calulated euclidean distance.
     */
    Var calculateDistance( const InputType& input )const {
        Var result ( 0 );
        for ( unsigned int i = 0; i < m_weights.size(); i++ ) {
            result += utils::pow ( m_weights[i] - input[i], 2 );
        }

        return utils::sqrt(result);
    }

    /// @brief getter for the weights
    /// @return the reference to the weights array.
    const InputType& getWeights()const{
      return m_weights;
    }
    
    /**
     * @brief will return the position of the Node in the map.
     * @return the position of the node.
     */
    const Position& getPosition() const {
        return m_position;
    }

    void applyWeightModifications ( Var learningRate, Var influence,const InputType& input ) {
      for ( unsigned int i=0; i<m_weights.size(); i++ ) {
	  m_weights[i] += learningRate * influence * ( input[i] - m_weights[i] );
      }
    }
};

}

}

#endif // NODE_H
